Data Driven Approaches for Healthcare
Machine learning for Identifying High Utilizers
Health care utilization routinely generates vast amounts of data from sources ranging from electronic medical records, insurance claims, vital signs, and patient-reported outcomes. Predicting health outcomes using data modeling approaches is an emerging field that can reveal important insights into disproportionate spending patterns. This book presents data driven methods, especially machine learning, for understanding and approaching the high utilizers problem, using the example of a large public insurance program. It describes important goals for data driven approaches from different aspects of the high utilizer problem, and identifies chal…
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Produktdetails
Weitere Autoren: Delcher, Chris / Shenkman, Elizabeth / Ranka, Sanjay
- ISBN: 978-1-00-070003-9
- EAN: 9781000700039
- Produktnummer: 31765558
- Verlag: Taylor & Francis Ltd.
- Sprache: Englisch
- Erscheinungsjahr: 2019
- Seitenangabe: 118 S.
- Plattform: PDF
- Masse: 10'124 KB
- Auflage: 1. Auflage
- Abbildungen: 25 schwarz-weiße Fotos
Über den Autor
Chengliang Yang, Department of Computer Science, University of Florida Chris Delcher, Institute of Child Health Policy, University of Florida Elizabeth Shenkman, Institute of Child Health Policy, University of Florida Sanjay Ranka, Department of Computer Science, University of Florida.
3 weitere Werke von Chengliang Yang:
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